See a full list of contributors
A beta version of the Social Science Reproduction Platform is now available! Sign up here if you would like to be part of our beta testing in the spring.
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/.
Computational reproducibilty is defined as the degree to which it is possible to obtain consistent results using the same input data, computational methods, and conditions of analysis (Sciences 2019). In 2019, the American Economic Association updated its Data and Code Availability Policy to require that the AEA Data Editor verify the reproducibility of all papers before they are accepted by an AEA journal. Similar policies have been adopted in political science, particularly at the American Journal of Political Science. In addition to the requirements laid out in such policies, specific recommendations were produced by data editors of social science journals to facilitate compliance. This change in policy is expected to improve the computational reproducibility of all published research going forward, after several studies showed that rates of computational reproducibility in the social sciences range from somewhat low to alarmingly low (Galiani, Gertler, and Romero 2018; Chang and Li 2015; Kingi et al. 2018).
Replication, or the process by which a study’s hypotheses and findings are re-examined using different data or different methods (or both) (King 1995) is an essential part of the scientific process that allows science to be “self-correcting.” Computational reproducibility, or the ability to reproduce the results, tables, and other figures of a paper using the available data, code, and materials, is a necessary condition for replication. Computational reproducibility is assessed through the process of reproduction. At the center of this process is the reproducer (you!), a party rarely involved in the production of the original paper. Reproductions sometimes involve the original author (whom we refer to as “the author”) in cases where additional guidance and materials are needed to execute the process.
This Guide is meant to be used in conjunction with the Social Science Reproduction Platform (SSRP), an open-source platform that crowdsources and catalogs attempts to assess and improve the computational reproducibility of published social science research. Though in its current version, the Guide is principally intended for reproductions of published research in economics, it may be used in other social science disciplines, and we welcome contributions that aim to “translate” any of its parts to other social science disciplines (learn how you can contribute here). The purpose of this document is to provide a common approach, terminology, and standards for conducting reproductions. The goal of reproductions, in general, is to assess and improve the computational reproducibility of published research in a way that promotes a better understanding of research and facilitates additional robustness checks, extensions, collaborations, and replications.
This Guide and the SSRP were developed as part of the Accelerating Computational Reproducibility in Economics (ACRE) project, which aims to assess, enable, and improve the computational reproducibility of published economics research. The ACRE project is led by the Berkeley Initiative for Transparency in the Social Sciences (BITSS)—an initiative of the Center for Effective Global Action (CEGA)—and Dr. Lars Vilhuber, Data Editor for the journals of the American Economic Association (AEA). This project is supported by the Laura and John Arnold Foundation.
View slides used for the presentation “How to Teach Reproducibility in Classwork”
Assessments of reproducibility can easily gravitate towards binary judgments that declare an entire paper “reproducible” or “non-reproducible.” These guidelines suggest a more nuanced approach by highlighting two realities that make binary judgments less relevant.
First, a paper may contain several scientific claims (or major hypotheses) that may vary in computational reproducibility. Each claim is tested using different methodologies, presenting results in one or more display items (outputs like tables and figures). Each display item will itself contain several specifications. Figure 0.1 illustrates this idea.